An AI model accurately detects cardiac structural issues using point-of-care ultrasound (POCUS) images, even by non-cardiologists.
Key Details
- 1The AI model was developed through a collaboration between AISAP and Sheba Medical Center.
- 2Training involved over 120,000 annotated transthoracic echocardiograms.
- 3AISAP Cardio, the POCUS platform, is FDA-cleared for automated cardiac measurements.
- 4Retrospective AUCs for key findings: mitral regurgitation (0.883), tricuspid regurgitation (0.913), ventricular dysfunction (0.940), reduced ejection fraction (0.982).
- 5Prospective real-world AUCs: 0.72, 0.87, 0.95, and 0.97 for the same metrics, using data from 200+ patients scanned by non-cardiologists.
Why It Matters
This AI-driven advancement opens opportunities for broader access to accurate cardiac screening at the point of care, enabling non-experts to detect significant heart issues and potentially improving patient outcomes, especially in resource-limited settings.
Source
Cardiovascular Business
Related News
•Radiology Business
Meta Funds Ohio State’s Radiology AI Innovation Projects
Meta will fund Ohio State researchers to explore AI-driven advancements in radiology, including chest X-ray reporting and disease prediction.
•AuntMinnie
ChatGPT-5 Performs Poorly at Detecting Distal Radius Fractures on X-rays
ChatGPT-5 missed about half of distal radius fractures on x-rays, showing limited diagnostic capability in a real-world scenario.
•AuntMinnie
AI Triage Tool Speeds Up ICH Head CT Turnaround in ED
AI-based triage for intracranial hemorrhage on head CT reduced emergency department report turnaround by 13 minutes.